Towards Sustainable Water Use: Experiences from the Projects AFRHINET and Baltic Flows

2014 ◽  
pp. 397-408
Author(s):  
Walter Leal Filho ◽  
Josep de la Trincheria ◽  
Johanna Vogt
Keyword(s):  
Water Use ◽  
Sustainable Water Use

How would irrigation enhance the global BECCS potential in view of sustainable water use?

2021 ◽  
Author(s):  
Zhipin Ai ◽  
Naota Hanasaki
Keyword(s):  
Water Use ◽  
Carbon Capture ◽  
Critical Role ◽  
Carbon Capture And Storage ◽  
Water Withdrawal ◽  
Water Usage ◽  
Sustainable Water Use ◽  
Additional Water ◽  
Sustainable Irrigation ◽  
And Storage

<p>Bioenergy with carbon capture and storage (BECCS) plays a critical role in many stringent scenarios targeting the 2°C goal. Although irrigation is considered a promising way to enhance BECCS potential while reducing the land requirement, it is still unknown where and to what extent it can enhance the global BECCS potential in view of sustainable water use. Based on integrated hydrological simulations, we found that sustainable irrigation without intervention in water usage for other sectors and refrain from exploiting nonrenewable water sources enhanced BECCS potential by only 5–6% (much smaller than 60–71% for unlimited irrigation) above the rainfed potential by the end of this century. Nonetheless, it adds limited additional water withdrawal (166–298 km<sup>3</sup> yr<sup>-1</sup>, corresponding to only 4–7% of the current total withdrawal) compared to that with unlimited irrigation (1392–3929 km<sup>3</sup> yr<sup>-1</sup>, corresponding to 35–98% of the current total withdrawal).</p>

Sustainable Water Use

2021 ◽  
pp. 153-189
Author(s):  
Seungho Lee
Keyword(s):  
Water Use ◽  
Sustainable Water Use

The Suitability of the Emerging Double Maize System Towards Sustainable Water Use under a Warm Temperature Continental Monsoon Climate

2019 ◽  
Vol 111 (4) ◽  
pp. 1946-1957
Author(s):  
Xingya Wang ◽  
Xiwei Liu ◽  
Qingzhao Wu ◽  
Pu Wang ◽  
Qingfeng Meng
Keyword(s):  
Water Use ◽  
Warm Temperature ◽  
Monsoon Climate ◽  
Sustainable Water Use

Scalable solutions to freshwater scarcity: Advancing theories of change to incentivise sustainable water use

2020 ◽  
Vol 9 ◽  
pp. 100055 ◽  
Author(s):  
D. Garrick ◽  
T. Iseman ◽  
G. Gilson ◽  
N. Brozovic ◽  
E. O'Donnell ◽  
...  
Keyword(s):  
Water Use ◽  
Sustainable Water Use ◽  
Theories Of Change

scholarly journals Watson on the Farm: Using Cloud-Based Artificial Intelligence to Identify Early Indicators of Water Stress

2019 ◽  
Vol 11 (22) ◽  
pp. 2645 ◽  
Author(s):  
Daniel Freeman ◽  
Shaurya Gupta ◽  
D. Hudson Smith ◽  
Joe Mari Maja ◽  
James Robbins ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Water Stress ◽  
Real Time ◽  
Water Use ◽  
Visual Recognition ◽  
Small Sample ◽  
Unmanned Aircraft ◽  
Minimal Amount ◽  
Sustainable Water Use ◽  
Early Indicators

As demand for freshwater increases while supply remains stagnant, the critical need for sustainable water use in agriculture has led the EPA Strategic Plan to call for new technologies that can optimize water allocation in real-time. This work assesses the use of cloud-based artificial intelligence to detect early indicators of water stress across six container-grown ornamental shrub species. Near-infrared images were previously collected with modified Canon and MAPIR Survey II cameras deployed via a small unmanned aircraft system (sUAS) at an altitude of 30 meters. Cropped images of plants in no, low-, and high-water stress conditions were split into four-fold cross-validation sets and used to train models through IBM Watson’s Visual Recognition service. Despite constraints such as small sample size (36 plants, 150 images) and low image resolution (150 pixels by 150 pixels per plant), Watson generated models were able to detect indicators of stress after 48 hours of water deprivation with a significant to marginally significant degree of separation in four out of five species tested (p < 0.10). Two models were also able to detect indicators of water stress after only 24 hours, with models trained on images of as few as eight water-stressed Buddleia plants achieving an average area under the curve (AUC) of 0.9884 across four folds. Ease of pre-processing, minimal amount of training data required, and outsourced computation make cloud-based artificial intelligence services such as IBM Watson Visual Recognition an attractive tool for agriculture analytics. Cloud-based artificial intelligence can be combined with technologies such as sUAS and spectral imaging to help crop producers identify deficient irrigation strategies and intervene before crop value is diminished. When brought to scale, frameworks such as these can drive responsive irrigation systems that monitor crop status in real-time and maximize sustainable water use.

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